Detection is a fundamental vision task that aims to localize and recognize objects in an image.
However, the data collection process of manually annotating bounding boxes or instance masks is tedious and costly, which limits the modern detection vocabulary size to an order of \(10^3\) . This is orders of magnitude smaller than the vocabulary humans use to describe the visual world.
To overcome such limitation, we focus on open-vocabulary object detection [1]}, [2]} to take detection beyond a fixed set of vocabulary.
